Methods and systems for providing personalized purchasing information

Abstract

A computer-implemented method for providing personalized purchasing information to a user may include: obtaining first identification data of the user from a first engine; verifying a registration status of the user based on the first identification data; obtaining second identification data of the user from a second engine based on the registration status of the user; comparing the first identification data of the user and the second identification data of the user; initiating a real-time credit monitoring session based on the comparison between the first identification data of the user and the second identification data of the user; generating the personalized purchasing information of the user based on the real-time credit score of the user; and demonstrating the personalized purchasing information on a display of a device associated with the user.

What is claimed is: 
     
       1. A computer-implemented method for providing personalized purchasing information to a user, the method comprising:
 receiving, via one or more processors, data indicative of an interaction between a user and a first engine; 
 obtaining, via the one or more processors, first identification data of the user from the first engine; 
 verifying, using the first identification data and via the one or more processors, a registration status of the user with a second engine; 
 based on the registration status of the user with the second engine, obtaining credit information of the user; 
 generating the personalized purchasing information of the user by inputting the credit information of the user into a trained machine learning model, wherein:
 the trained machine learning model has been trained, based on credit information of customers other than the user and on personalized purchasing information of the customers other than the user, to generate output that includes the personalized purchasing information of the user in response to an input of the credit information of the user; 
 the personalized purchasing information generated by the machine learning model includes:
 a prequalification status of the user based on the credit information of the user; and 
 a purchasing recommendation for the user based on the prequalification status of the user, the purchasing recommendation including a priced financing option for one or more products, the priced financing option requiring the prequalification status in order to be priced for the user; 
 
 
 generating a web page that is specific to the user and that includes the personalized purchasing information generated by the trained machine learning model; and 
 causing, via the first engine, a display of a device associated with the user to display the generated web page, such that the personalized purchasing information is provided in response to the interaction between the user and the first engine. 
 
     
     
       2. The computer-implemented method of  claim 1 , wherein verifying the registration status of the user with the second engine includes comparing the first identification data of the user from the first engine with second identification data of the user from the second engine. 
     
     
       3. The computer-implemented method of  claim 1 , wherein obtaining the credit information of the user includes obtaining, via the second engine, a most-recent periodic credit score of the user. 
     
     
       4. The computer-implemented method of  claim 1 , wherein obtaining the credit information of the user includes:
 initiating, via the second engine, a real-time credit monitoring session for the user; and 
 generating a real-time credit score for the user based on the real-time credit monitoring session. 
 
     
     
       5. The computer-implemented method of  claim 1 , further comprising:
 receiving further data indicative of further user interaction associated with the first engine via the device associated with the user, the further interaction associated with the personalized purchasing information included in the generated web page. 
 
     
     
       6. The computer-implemented method of  claim 1 , wherein the second engine is associated with one or more credit monitoring agencies. 
     
     
       7. The computer-implemented method of  claim 1 , wherein the first engine is associated with one or more loan prequalifying agencies. 
     
     
       8. The computer-implemented method of  claim 1 , wherein the personalized purchasing information of the user further includes at least one of an annual percentage rate, a loan interest rate, or a price range of purchasing a product of the one or more products. 
     
     
       9. The computer-implemented method of  claim 1 , wherein the one or more products include one or more vehicles. 
     
     
       10. The computer-implemented method of  claim 1 , wherein the interaction between the user and the first engine includes a search query by the user for a product of a same type as the one or more products. 
     
     
       11. A system for providing personalized purchasing information to a user, the system comprising:
 at least one memory storing instructions and a trained machine learning model; and 
 at least one processor operatively connected to the at least one memory and configured to execute the instructions to perform operations, including:
 receiving data indicative of an interaction between a user and a first engine; 
 obtaining first identification data of the user from the first engine; 
 verifying, using the first identification data, a registration status of the user with a second engine; 
 based on the registration status of the user with the second engine, obtaining credit information of the user; 
 generating the personalized purchasing information of the user by inputting the credit information of the user into the trained machine learning model, wherein:
 the trained machine learning model has been trained, based on credit information of customers other than the user and on personalized purchasing information of the customers other than the user, to generate output that includes the personalized purchasing information of the user in response to an input of the credit information of the user; 
 the personalized purchasing information generated by the machine learning model includes:
 a prequalification status of the user based on the credit information of the user; and 
 a purchasing recommendation for the user based on the prequalification status of the user, the purchasing recommendation including a priced financing option for one or more products, the priced financing option requiring the prequalification status in order to be priced for the user; 
 
 
 generating a web page that is specific to the user and that includes the personalized purchasing information generated by the trained machine learning model; and 
 causing, via the first engine, a display of a device associated with the user to display the generated web page, such that the personalized purchasing information is provided in response to the interaction between the user and the first engine. 
 
 
     
     
       12. The system of  claim 11 , wherein verifying the registration status of the user with the second engine includes comparing the first identification data of the user from the first engine with second identification data of the user from the second engine. 
     
     
       13. The system of  claim 11 , wherein obtaining the credit information of the user includes obtaining, via the second engine, a most-recent periodic credit score of the user. 
     
     
       14. The system of  claim 11 , wherein obtaining the credit information of the user includes:
 initiating, via the second engine, a real-time credit monitoring session for the user; and 
 generating a real-time credit score for the user based on the real-time credit monitoring session. 
 
     
     
       15. The system of  claim 11 , wherein the operations further include:
 receiving further data indicative of further user interaction associated with the first engine via the device associated with the user, the further interaction associated with the personalized purchasing information included in the generated web page. 
 
     
     
       16. The system of  claim 11 , wherein the second engine is associated with one or more credit monitoring agencies. 
     
     
       17. The system of  claim 11 , wherein the first engine is associated with one or more loan prequalifying agencies. 
     
     
       18. The system of  claim 11 , wherein:
 the personalized purchasing information of the user further includes at least one of an annual percentage rate, a loan interest rate, or a price range of purchasing a product of the one or more products; and 
 the one or more products include one or more vehicles. 
 
     
     
       19. The system of  claim 11 , wherein the interaction between the user and the first engine includes a search query by the user for a product of a same type as the one or more products. 
     
     
       20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processers, cause the one or more processors to perform a method for providing personalized purchasing information to a user, the method comprising:
 receiving, via one or more processors, data indicative of an interaction between a user and a first engine, the interaction including a search query by the user for a vehicle; 
 obtaining, via the one or more processors, first identification data of the user from the first engine associated with a loan prequalification entity; 
 verifying, using the first identification data and via the one or more processors, a registration status of the user with a second engine associated with a credit monitoring entity; 
 based on the registration status of the user with the second engine, obtaining credit information of the user; 
 generating the personalized purchasing information of the user by inputting the credit information of the user into a trained machine learning model, wherein:
 the trained machine learning model has been trained, based on credit information of customers other than the user and on personalized purchasing information of the customers other than the user, to generate output that includes the personalized purchasing information of the user in response to an input of the credit information of the user; 
 the personalized purchasing information generated by the machine learning model includes:
 a prequalification status of the user based on the credit information of the user; and 
 a purchasing recommendation for the user based on the prequalification status of the user, the purchasing recommendation including a priced financing option for one or more vehicles, the priced financing option requiring the prequalification status in order to be priced for the user; 
 
 
 generating a web page that is specific to the user and that includes the personalized purchasing information generated by the trained machine learning model; and 
 causing, via the first engine, a display of a device associated with the user to display the generated web page, such that the personalized purchasing information is provided in response to the interaction between the user and the first engine.